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Open access publications by faculty, staff, postdocs, and graduate students from the Delaware Geological Survey.
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Browsing Open Access Publications by Author "Vargas, Rodrigo"
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Item Downscaling satellite soil moisture for landscape applications: A case study in Delaware, USA(Journal of Hydrology: Regional Studies, 2021-10-15) Warner, Daniel L.; Guevara, Mario; Callahan, John; Vargas, RodrigoStudy region: Delaware, USA and its surrounding watersheds. Study focus: An ensemble using multiple Kernel K-nearest neighbors (KKNN) models was trained to predict daily grids of SSM at 100-meter resolution based on SSM estimates from the European Space Agency’s Climate Change Initiative Soil Moisture Product, terrain data, soil maps, and local meteorological network data. Estimated SSM was evaluated against independent in situ SSM observations and were investigated for relationships with land cover class and vegetation phenology (i.e., NDVI). New hydrological insights for the region Downscaled daily mean SSM estimates had lower error in space (27%) and greater predictive performance over time compared to the raw, coarse resolution remotely sensed SSM dataset when calibrated to field observed values. Downscaled SSM identified stronger and more widespread temporal relationships with NDVI than other estimation methods. However, both coarse and fine resolution datasets greatly underestimated SSM in wetland areas. The findings highlight the need for enhanced in situ SSM monitoring across diverse settings to improve landscape-level downscaled SSM. The downscaling methodology developed in this study was able to produce daily SSM estimates, providing a framework that can support future SSM modeling efforts, hydroecological investigations, and agricultural studies in this and other regions around the world when used in conjunction with ground-based monitoring networks.Item Spatial biases of information influence global estimates of soil respiration: How can we improve global predictions?(Global Change Biology, 2021-05-01) Stell, Emma; Warner, Daniel; Jian, Jinshi; Bond-Lamberty, Ben; Vargas, RodrigoSoil respiration (Rs), the efflux of CO2 from soils to the atmosphere, is a major component of the terrestrial carbon cycle, but is poorly constrained from regional to global scales. The global soil respiration database (SRDB) is a compilation of in situ Rs observations from around the globe that has been consistently updated with new measurements over the past decade. It is unclear whether the addition of data to new versions has produced better-constrained global Rs estimates. We compared two versions of the SRDB (v3.0 n = 5173 and v5.0 n = 10,366) to determine how additional data influenced global Rs annual sum, spatial patterns and associated uncertainty (1 km spatial resolution) using a machine learning approach. A quantile regression forest model parameterized using SRDBv3 yielded a global Rs sum of 88.6 Pg C year−1, and associated uncertainty of 29.9 (mean absolute error) and 57.9 (standard deviation) Pg C year−1, whereas parameterization using SRDBv5 yielded 96.5 Pg C year−1 and associated uncertainty of 30.2 (mean average error) and 73.4 (standard deviation) Pg C year−1. Empirically estimated global heterotrophic respiration (Rh) from v3 and v5 were 49.9–50.2 (mean 50.1) and 53.3–53.5 (mean 53.4) Pg C year−1, respectively. SRDBv5’s inclusion of new data from underrepresented regions (e.g., Asia, Africa, South America) resulted in overall higher model uncertainty. The largest differences between models parameterized with different SRDVB versions were in arid/semi-arid regions. The SRDBv5 is still biased toward northern latitudes and temperate zones, so we tested an optimized global distribution of Rs measurements, which resulted in a global sum of 96.4 ± 21.4 Pg C year−1 with an overall lower model uncertainty. These results support current global estimates of Rs but highlight spatial biases that influence model parameterization and interpretation and provide insights for design of environmental networks to improve global-scale Rs estimates.